Feature Selection for Better Spectral Characterization or: How I Learned to Start Worrying and Love Ensembles
An ever-looming threat to astronomical applications of machine learning is the danger of over-fitting data, also known as the `curse of dimensionality.' This occurs when there are fewer samples than the number of independent variables. In this work, we focus on the problem of stellar parameterization from low-mid resolution spectra, with blended absorption lines. We address this problem using an iterative algorithm to sequentially prune redundant features from synthetic PHOENIX spectra, and arrive at an optimal set of wavelengths with the strongest correlation with each of the output variables -- T$_{\rm eff}$, $\log g$, and [Fe/H]. We find that at any given resolution, most features (i.e., absorption lines) are not only redundant, but actually act as noise and decrease the accuracy of parameter retrieval.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionRetrievalSimilar Papers 제목 키워드 기반
Joint Characterization of the Cryospheric Spectral Feature Space
Hyperspectral feature spaces are useful for many remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In such cases, characterization of the feature space dimensionality…
ClusteringDimensionality ReductionFast forward feature selection for the nonlinear classification of hyperspectral images
A fast forward feature selection algorithm is presented in this paper. It is based on a Gaussian mixture model (GMM) classifier. GMM are used for classifying hyperspectral images. The algorithm selects iteratively spectr…
Classificationfeature selectionGeneral ClassificationHyperspectral Image ClassificationRandom Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees
Random Fourier features is one of the most popular techniques for scaling up kernel methods, such as kernel ridge regression. However, despite impressive empirical results, the statistical properties of random Fourier fe…
regressionManiFeSt: Manifold-based Feature Selection for Small Data Sets
In this paper, we present a new method for few-sample supervised feature selection (FS). Our method first learns the manifold of the feature space of each class using kernels capturing multi-feature associations. Then, b…
feature selectionMultispectral Spatial Characterization: Application to Mitosis Detection in Breast Cancer Histopathology
Accurate detection of mitosis plays a critical role in breast cancer histopathology. Manual detection and counting of mitosis is tedious and subject to considerable inter- and intra-reader variations. Multispectral imagi…
General ClassificationMitosis Detection